Lets discuss reproducible report.
Check weather the observation we have are significantly differ from their mean.
Let have weight of birds from yesterdays survey. Suppose we have observation of weight always less than 70. Test the hypothesis now with this new data.
## [1] 50 55 60 63 65 66 72 75 80
##
## One Sample t-test
##
## data: wts
## t = 20.464, df = 8, p-value = 3.402e-08
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 57.77401 72.44821
## sample estimates:
## mean of x
## 65.11111
##
## One Sample t-test
##
## data: wts
## t = -1.5365, df = 8, p-value = 0.08148
## alternative hypothesis: true mean is less than 70
## 99 percent confidence interval:
## -Inf 74.32689
## sample estimates:
## mean of x
## 65.11111
wts <- c(50, 55, 60, 63, 65, 66, 72, 75)
wts_2 <- c(57, 63, 66, 72, 73, 80, 92, 95)
# independent 2-group t-test
ttest = t.test(wts,wts_2)
names(ttest)## [1] "statistic" "parameter" "p.value" "conf.int" "estimate"
## [6] "null.value" "stderr" "alternative" "method" "data.name"
##
## Paired t-test
##
## data: wts and wts_2
## t = -5.65, df = 7, p-value = 0.0007745
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -16.312959 -6.687041
## sample estimates:
## mean of the differences
## -11.5
Create a dataframe
## wts wts_2
## Min. :50.00 Min. :57.00
## 1st Qu.:58.75 1st Qu.:65.25
## Median :64.00 Median :72.50
## Mean :63.25 Mean :74.75
## 3rd Qu.:67.50 3rd Qu.:83.00
## Max. :75.00 Max. :95.00
## Call:
## aov(formula = values ~ ind, data = wt_stack)
##
## Terms:
## ind Residuals
## Sum of Squares 529 1755
## Deg. of Freedom 1 14
##
## Residual standard error: 11.1963
## Estimated effects may be unbalanced
Summary
## Df Sum Sq Mean Sq F value Pr(>F)
## ind 1 529 529.0 4.22 0.0591 .
## Residuals 14 1755 125.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## mpg cyl disp hp drat wt
## mpg 1.0000000 -0.8521620 -0.8475514 -0.7761684 0.68117191 -0.8676594
## cyl -0.8521620 1.0000000 0.9020329 0.8324475 -0.69993811 0.7824958
## disp -0.8475514 0.9020329 1.0000000 0.7909486 -0.71021393 0.8879799
## hp -0.7761684 0.8324475 0.7909486 1.0000000 -0.44875912 0.6587479
## drat 0.6811719 -0.6999381 -0.7102139 -0.4487591 1.00000000 -0.7124406
## wt -0.8676594 0.7824958 0.8879799 0.6587479 -0.71244065 1.0000000
## qsec 0.4186840 -0.5912421 -0.4336979 -0.7082234 0.09120476 -0.1747159
## vs 0.6640389 -0.8108118 -0.7104159 -0.7230967 0.44027846 -0.5549157
## am 0.5998324 -0.5226070 -0.5912270 -0.2432043 0.71271113 -0.6924953
## gear 0.4802848 -0.4926866 -0.5555692 -0.1257043 0.69961013 -0.5832870
## carb -0.5509251 0.5269883 0.3949769 0.7498125 -0.09078980 0.4276059
## qsec vs am gear carb
## mpg 0.41868403 0.6640389 0.59983243 0.4802848 -0.55092507
## cyl -0.59124207 -0.8108118 -0.52260705 -0.4926866 0.52698829
## disp -0.43369788 -0.7104159 -0.59122704 -0.5555692 0.39497686
## hp -0.70822339 -0.7230967 -0.24320426 -0.1257043 0.74981247
## drat 0.09120476 0.4402785 0.71271113 0.6996101 -0.09078980
## wt -0.17471588 -0.5549157 -0.69249526 -0.5832870 0.42760594
## qsec 1.00000000 0.7445354 -0.22986086 -0.2126822 -0.65624923
## vs 0.74453544 1.0000000 0.16834512 0.2060233 -0.56960714
## am -0.22986086 0.1683451 1.00000000 0.7940588 0.05753435
## gear -0.21268223 0.2060233 0.79405876 1.0000000 0.27407284
## carb -0.65624923 -0.5696071 0.05753435 0.2740728 1.00000000
Packages are collection of several functions in R.
To use a particular function from a package, it need to be downloaded and loaded.
This session introduces the essential of package discovery, installation, and use.
The web site at https://cran.r-project.org/ contains descriptions of all packages, as well as essential reference materials.
A total of 2,400 packages comprising over 3,000 functions have been used in ecology and evolution, and the journal Methods in Ecology and Evolution for instance had the highest reported proportionate usage of all journals recently examined in a recent study (Lai et al., 2019). - A checklist for choosing between R packages in ecology and evolution
The number of R packages for common statistical and ecological/evolutionary concepts.
One of the main reasons data analysts turn to R is for its strong graphic capabilities.
## The following object is masked from package:ggplot2:
##
## mpg
Few plotting functions comes built-in when R installed. These can help do diffrent type of plots. Such as * Scater Plots * Dot Plots * Bar Plots * Pie Charts * Box Plots
dotchart(mtcars$mpg,labels=row.names(mtcars),cex=.7,
main="Gas Milage for Car Models",
xlab="Miles Per Gallon")slices <- c(10, 12,4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie(slices, labels = lbls, main="Pie Chart of Countries")boxplot(mpg~cyl,data=mtcars, main="Car Milage Data",
xlab="Number of Cylinders", ylab="Miles Per Gallon")boxplot(len~supp*dose, data=ToothGrowth, notch=TRUE,
col=(c("gold","darkgreen")),
main="Tooth Growth", xlab="Suppliment and Dose")## Warning in bxp(list(stats = structure(c(8.2, 9.7, 12.25, 16.5, 21.5, 4.2, : some
## notches went outside hinges ('box'): maybe set notch=FALSE
Graphical parameters describes how to change a graph’s symbols, fonts, colors, and lines. Axes and text describe how to customize a graph’s axes, add reference lines, text annotations and a legend. Combining plots describes how to organize multiple plots into a single graph.
## The following objects are masked from mtcars (pos = 3):
##
## am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object is masked from package:ggplot2:
##
## mpg
boxplot(mpg~cyl, main="Milage by Car Weight",
yaxt="n", xlab="Milage", horizontal=TRUE,
col=terrain.colors(3))
legend("topright", inset=.05, title="Number of Cylinders",
c("4","6","8"), fill=terrain.colors(3), horiz=TRUE) Created and Maintained by Sangram Keshari Sahu
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Licensed under CC-BY 4.0
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